Abstract: Multiplexed Tissue Imaging (MTI) techniques have revolutionized tissue sample analysis by enabling simultaneous measurement of numerous biomarkers. However, challenges such as technical artifacts, tissue loss, long acquisition times, and limitations in current MTI analyses hinder its full potential. In this talk, we will address these challenges and propose solutions to enhance the capabilities and accessibility of MTI, with a focus on representation learning. Our emphasis lies in the need for comprehensive representations of multiplexed single-cell images, encompassing morphology, cell shape, and texture beyond mean intensity features. We will also explore techniques like image-to-image translation and image-to-omics integration to obtain transferable multimodal representations, facilitating a holistic interpretation of cellular data. Through the utilization of representation learning on MTI, we uncover diagnostically significant features in standard histopathology images, advancing our understanding of tumor biology and improving cancer diagnosis and treatment. Furthermore, we will discuss strategies to overcome cost and time challenges associated with MTI, making it more accessible in cancer research and clinical settings. These advancements propel the field forward, unlocking the potential of MTI in cancer diagnosis and treatment, driving scientific discoveries, and ultimately improving patient outcomes.